Regular Expression Complete Guide

Regular expressions or regex puts a lot of people off, just because of its look at first glance. But once you master this it will open a whole new different level of doing string manipulation and the best part of it is that it can be used with mostly all of the programming language as well as with Linux commands. It can be used to find any kind of pattern that you can think of within the text and once you find the text you can do pretty much whatever you want to do with that text. By this example, you can get an idea of how powerful and useful regex is.
What is Regex?
If you are reading this post then most probably you already know what a regex is, if you don’t know here is a quick and easy definition
Regex stands for Regular Expression and is essentially an easy way to define a pattern of characters. The most common use of regex is in pattern identification, text mining, or input validation.

#regular-expressions #python #regex #python-regex #pattern-finding

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Regular Expression Complete Guide

Mad Libs: Using regular expressions

From Tiny Python Projects by Ken Youens-Clark

Everyone loves Mad Libs! And everyone loves Python. This article shows you how to have fun with both and learn some programming skills along the way.


Take 40% off Tiny Python Projects by entering fccclark into the discount code box at checkout at manning.com.


When I was a wee lad, we used to play at Mad Libs for hours and hours. This was before computers, mind you, before televisions or radio or even paper! No, scratch that, we had paper. Anyway, the point is we only had Mad Libs to play, and we loved it! And now you must play!

We’ll write a program called mad.py  which reads a file given as a positional argument and finds all the placeholders noted in angle brackets like <verb>  or <adjective> . For each placeholder, we’ll prompt the user for the part of speech being requested like “Give me a verb” and “Give me an adjective.” (Notice that you’ll need to use the correct article.) Each value from the user replaces the placeholder in the text, and if the user says “drive” for “verb,” then <verb>  in the text replaces with drive . When all the placeholders have been replaced with inputs from the user, print out the new text.

#python #regular-expressions #python-programming #python3 #mad libs: using regular expressions #using regular expressions

Madyson  Reilly

Madyson Reilly

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Regular Expressions: What and Why?

Regular expressions is a powerful search and replace technique that you probably have used even without knowing. Be it your text editor’s “Find and Replace” feature, validation of your http request body using a third party npm module or your terminal’s ability to return list of files based on some pattern, all of them use Regular Expressions in one way or the other. It is not a concept that programmers must definitely learn but by knowing it you are able to reduce the complexity of your code in some cases.

_In this tutorial we will be learning the key concepts as well as some use cases of Regular Expressions in _javascript.

How do you write a Regular Expression?

There are two ways of writing Regular expressions in Javascript. One is by creating a **literal **and the other is using **RegExp **constructor.

//Literal
const myRegex=/cat/ig

//RegExp
const myRegex=new RegExp('cat','ig')

While both types of expressions will return the same output when tested on a particular string, the benefit of using the RegExp constructor is that it is evaluated at runtime hence allowing use of javascript variables for dynamic regular expressions. Moreover as seen in this benchmark test the RegExp constructor performs better than the literal regular expression in pattern matching.

The syntax in either type of expression consists of two parts:

  • pattern : The pattern that has to be matched in a string.
  • flags : these are modifiers which are rules that describe how pattern matching will be performed.

#regular-expressions #javascript #programming #js #regex #express

Regular Expressions in Python [With Examples]: How to Implement?

While processing raw data from any source, extracting the right information is important so that meaningful insights can be obtained from the data. Sometimes it becomes difficult to take out the specific pattern from the data especially in the case of textual data.

The textual data consist of paragraphs of information collected via survey forms, scrapping websites, and other sources. The Channing of different string accessors with pandas functions or other custom functions can get the work done, but what if a more specific pattern needs to be obtained? Regular expressions do this job with ease.

What is a Regular Expression (RegEx)?

Examples to Understand The Workaround

How to Implement it in Python?

Conclusion

#data science #python #regular expression #regular expression in python

Regular Expression Complete Guide

Regular expressions or regex puts a lot of people off, just because of its look at first glance. But once you master this it will open a whole new different level of doing string manipulation and the best part of it is that it can be used with mostly all of the programming language as well as with Linux commands. It can be used to find any kind of pattern that you can think of within the text and once you find the text you can do pretty much whatever you want to do with that text. By this example, you can get an idea of how powerful and useful regex is.
What is Regex?
If you are reading this post then most probably you already know what a regex is, if you don’t know here is a quick and easy definition
Regex stands for Regular Expression and is essentially an easy way to define a pattern of characters. The most common use of regex is in pattern identification, text mining, or input validation.

#regular-expressions #python #regex #python-regex #pattern-finding

Regular Expressions in Python [With Examples]: How to Implement? | upGrad blog

While processing raw data from any source, extracting the right information is important so that meaningful insights can be obtained from the data. Sometimes it becomes difficult to take out the specific pattern from the data especially in the case of textual data.

The textual data consist of paragraphs of information collected via survey forms, scrapping websites, and other sources. The Channing of different string accessors with pandas functions or other custom functions can get the work done, but what if a more specific pattern needs to be obtained? Regular expressions do this job with ease.

What is a Regular Expression (RegEx)?

A regular expression is a representation of a set of characters for strings. It presents a generalized formula for a particular pattern in the strings which helps in segregating the right information from the pool of data. The expression usually consists of symbols or characters that help in forming the rule but, at first glance, it may seem weird and difficult to grasp. These symbols have associated meanings that are described here.

Meta-characters in RegEx

  1. ‘.’: is a wildcard, matches a single character (any character, but just once)
  2. ^: denotes start of the string
  3. $: denotes the end of the string
  4. [ ]: matches one of the sets of characters within [ ]
  5. [a-z]: matches one of the range of characters a,b,…,z
  6. [^abc] : matches a character that is not a,b or c.
  7. a|b: matches either a or b, where a and b are strings
  8. () : provides scoping for operators
  9. \ : enables escape for special characters (\t, \n, \b, .)
  10. \b: matches word boundary
  11. \d : any digit, equivalent to [0-9]
  12. \D: any non digit, equivalent to [^0-9]
  13. \s : any whitespace, equivalent to [ \t\n\r\f\v]
  14. \S : any non-whitespace, equivalent to [^\t\n\r\f\v]
  15. \w : any alphanumeric, equivalent to [a-zA-Z0-9_]
  16. \W : any non-alphanumeric, equivalent to [^a-zA-Z0-9_]
  17. ‘*’: matches zero or more occurrences
  18. ‘+’: matches one or more occurrences
  19. ‘?’: matches zero or one occurrence
  20. {n}: exactly n repetitions, n>=0
  21. {n,}: at least n repetitions
  22. {,n}: at most n repetitions
  23. {m,n}: at least m repetitions and at most n repetitions

#data science #python #regular expression #regular expression in python